Seismic design of low- and medium-rise chevron braced steel frames
Bibliographic record
Abstract
This paper presents two different seismic design approaches for multistorey chevron (inverted V) steel braced frames. The first method complies with current Canadian code provisions in which the beams in the bracing bents must be designed to sustain the forces expected to develop up to buckling of the bracing members. In the second approach, the beams must also resist the gravity loads together with a fraction of the brace loads that are induced after buckling of the braces. This second approach aims at minimizing the degradation in storey shear resistance typically exhibited by chevron bracing subjected to strong ground motions, and it is proposed that such braced frames with reinforced beams be designed for reduced seismic loads. Both design procedures are applied to typical multistorey braced frames to examine their economical impacts. Three different beam strength levels were considered for the second design method. The results show that the saving expected from reducing the seismic loads in the second design approach is generally offset by the increase in beam sizes required by this method. However, the braced frames with stronger beams exhibit a much higher storey shear resistance after buckling of the bracing members has occurred.Key words: earthquakes, seismic, design, steel, structures, braced frames, bracing members, beams, columns, connections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".